Method and System for Pushing Mobile Application

1. A method for pushing a mobile application, comprising: calculating, according to information about mobile applications operated by each user in a mobile application store and a pre-generated relevance between the mobile applications, a relevance between mobile application categories in the mobile application store, said calculating including calculating the relevance between first and second mobile application categories of the mobile application categories based on: multiplying a coefficient with the pre-generated relevance between each pair of the mobile applications in the first and second mobile application categories, the each pair including two mobile applications respectively from the first and second mobile application categories; and summing a result of the multiplying of each pair; obtaining a designated mobile application searched or downloaded from the mobile application store by a user; determining more than one mobile application category with top relevance to a mobile application category to which the designated mobile application belongs; calculating, according to pre-generated weight values of mobile applications, a degree of recommendation of each mobile application under the determined mobile application category; and selecting, according to a principle of high to low of the degree of recommendation of each mobile application under the determined mobile application category, a preset recommendation result number of mobile applications as a recommendation result and pushing the recommendation result.

2. The method of claim 1 , wherein said determining comprises: classifying, according to category information about each mobile application in a mobile application ontology base, the mobile applications operated by each user.

3. The method of claim 1 , wherein said calculating the relevance between the mobile application categories includes pre-generating the pre-generated relevance between the mobile applications by: calculating a relevance R(app m ,app n ) between the mobile applications app m and app n in accordance with: R ⁡ ( app m , app n ) = ∑ u = 1 U ⁢ ⁢ w u × s app m × ( k 1 + 1 ) s app m + K × s app n × ( k 2 + 1 ) s app n + k 2 where U represents a user set operating the mobile application app m and the mobile application app n simultaneously, and s app m , and s app n respectively represent score values allocated by a user u in the user set U for the app m and app n ; w u represents a weight of the user u in the user set U, K = k 1 × ( 1 - b + b × n u n avg ) , k 1 and k 2 are preset adjustment factors, n u represents a total number of mobile applications operated by the user u in the user set U, b is an adjustment factor, and n avg represents an average of the total number of mobile applications operated by the user u.

4. The method of claim 3 , wherein the weight w u of the user u in the user set U is w u = log ⁢ N - n u + 0.5 n u + 0.5 ; where N represents a total number of mobile applications operated by each user.

5. The method of claim 3 , wherein the score value s app m allocated by the user u for the mobile application app m is: s app m = ∑ t = 1 T ⁢ ⁢ s t × B t , app m ; where t represents a t th type of operating the mobile application app m , T represents a total number of types of operating the mobile application app m , and s t represents a basic score of the user u operating the mobile application app m ; B t,app m includes at least one of an indication value of whether the user u performs the t th type of operation on the mobile application app m and duration information about the user u performing the t th type of operation on the mobile application app m .

6. The method of claim 1 , wherein said calculating the relevance between the mobile application categories comprises: calculating a relevance R(concept i ,concept j ) between the first mobile application category concept i and the second mobile application category concept j in accordance with: R ⁡ ( concept i , concept j ) = ∑ concept app m = concept i , ⁢ concept app n = concept j U ⁢ ⁢ f app m ⁢ app n f app m + f app n × R ⁡ ( app m , app n ) where concept i and concept j are respectively the mobile application categories to which the first mobile application app m and the second mobile application app n belong, R(app m ,app n ) is a relevance between the first mobile application app m and the second mobile application app n , f app m represents a total number of users operating the first mobile application app m , f app n represents a total number of users operating the second mobile application app n , and f app m app n represents a total number of users contained in an intersection of a user set operating the first mobile application app m and a user set operating the second mobile application app n .

7. The method of claim 1 , wherein said calculating the relevance between the mobile application categories includes determining the pre-generating weight values of the mobile applications by: calculating a weight value w concept i app m of a mobile application app m in a mobile application category concept i in accordance with: w concept i ⁢ app m = ∑ t = 1 T ⁢ ⁢ g t × A t , app m A t , concept i where t represents a t th type of operating the mobile application app m , T represents a total number of types of operating the mobile application app m , A t,app m represents at least one of a total number of times and a total duration of the mobile application app m being operated by the t th type in a user history log, A t,concept i represents at least one of total number of times and a total duration of all of the mobile applications under the mobile application category concept i being operated by the t th type in the user history log; and g t represents an impact factor corresponding to the mobile application app m operated by the t th type in the user history log.

8. The method of claim 7 , wherein the operating on the mobile application app m comprise at least one of viewing, downloading and using.

9. The method of claim 1 , further comprising: adding a newly-added mobile application in a mobile application store to a mobile application ontology base; labeling corresponding category information and attribute information for the newly-added mobile application; multiplying an average weight value of top-ranked mobile applications under the mobile application category to which the newly added mobile application belongs by a preset attenuation factor; and obtaining a weight value of the newly added mobile application based upon said multiplying.

10. The method of claim 1 , wherein said calculating the degree of recommendation of each mobile application comprises: calculating a degree of recommendation rec app m app n of recommending the mobile application app n to the user based upon rec app m app n =R(concept i ,concept j )×w concept j app n +k×comatt(app m ,app n ) where app m is the designated mobile application, the mobile application category to which the designated mobile application app m belongs is concept i , the mobile application category to which the mobile application app n belongs is concept j , the mobile application category concept j belongs to the determined mobile application category, R(concept i ,concept j ) is the relevance between the mobile application category concept i and the mobile application category concept j , w concept j app n is a weight value of the mobile application app n under the mobile application category concept j , comatt(app m ,app n ) is a number of identical attributes of the mobile application app n and the mobile application app m , and k is a preset impact factor.

11. The method of claim 1 , wherein said selecting the preset recommendation result number of the mobile applications comprises: respectively extracting mobile applications with top-ranked degrees of recommendation from the determined mobile application category; ranking the extracted mobile applications in an order from high to low of the degree of recommendation; and taking a predetermined number of top-ranked mobile applications as the recommendation result; and pushing the predetermined number of the top-ranked mobile applications.

12. A system for pushing a mobile application, comprising: a processor; and a memory having one or more programs stored thereon for instructing said processor, the one or more programs including: instruction for calculating, according to information about mobile applications operated by each user in a mobile application store and a pre-generated relevance between the mobile applications, a relevance between mobile application categories in the mobile application store, the relevance between first and second mobile application categories of the mobile application categories being based on: multiplying a coefficient with the pre-generated relevance between each pair of the mobile applications in the first and second mobile application categories, the each pair including two mobile applications respectively from the first and second mobile application categories; and summing a result of the multiplying of each pair; instruction for obtaining a designated mobile application searched or downloaded from the mobile application store by a user; instruction for determining more than one mobile application category with top relevance to a mobile application category to which the designated mobile application belongs; instruction for calculating, according to pre-generated weight values of mobile applications, a degree of recommendation of each mobile application under the determined mobile application category; and instruction for selecting, according to a principle of high to low of a degree of recommendation of each mobile application under the determined mobile application category, a preset recommendation result number of the mobile applications as a recommendation result and pushing the recommendation result.

13. The system of claim 12 , wherein the one or more programs includes instruction for classifying, according to category information about each mobile application in a mobile application ontology base, the mobile applications operated by each user.

14. The system of claim 12 , wherein the one or more programs includes instruction for calculating the relevance between mobile application categories by calculating a relevance R(concept i ,concept j ) between the first mobile application category concept i and the second mobile application category concept j based upon: R ⁡ ( concept i , concept j ) = ∑ concept app m = concept i , ⁢ concept app n = concept j U ⁢ ⁢ f app m ⁢ app n f app m + f app n × R ⁡ ( app m , app n ) where concept i and concept j are respectively the mobile application categories to which the first mobile application app m and the second mobile application app n belong, R(app m ,app n ) is a relevance between the first mobile application app m and the second mobile application app n , f app m represents a total number of users operating the first mobile application app m , f app n represents a total number of users operating the second mobile application app n , and f app m app n represents a total number of users contained in an intersection of a user set operating the first mobile application app m and a user set operating the second mobile application app n .

15. The system of claim 12 , wherein the one or more programs includes instruction for pre-generating weight values of mobile applications by calculating a weight value w concept i app m of a mobile application app m in the mobile application category concept i based upon: w concept i ⁢ app m = ∑ t = 1 T ⁢ ⁢ g t × A t , app m A t , concept i where t represents a t th type of operating the mobile application app m , T represents a total number of types of operating the mobile application app m , A t,app m represents at least one of a total number of times and a total duration of the mobile application app m being operated by the t th type in a user history log, A t,concept i represents at least one of total number of times and a total duration of all of the mobile applications under the mobile application category concept i being operated by the t th type in the user history log; and g t represents an impact factor corresponding to the mobile application app m operated by the t th type in the user history log.

16. The system of claim 15 , wherein the operating on the mobile application comprise at least one of viewing, downloading and using.

17. The system of claim 12 , wherein the one or more programs includes: instruction for adding a newly-added mobile application in a mobile application store to a mobile application ontology base and labeling corresponding category information and attribute information for the newly-added mobile application; and instruction for multiplying an average weight value of top-ranked mobile applications under the mobile application category to which the newly added mobile application belongs by a preset attenuation factor so as to obtain a weight value of the newly-added mobile application.

18. The system of claim 12 , wherein the one or more programs includes instruction for calculating the degree of recommendation of a mobile application under said mobile application category by calculating a degree of recommendation rec app m app n of recommending a mobile application app n to the user based upon rec app m app n =R(concept i , concept j )×w concept j app n +k×comatt(app m ,app n ) where app m is the designated mobile application, the mobile application category to which the designated mobile application app m belongs is concept i , the mobile application category to which the mobile application app n belongs is concept j , the mobile application category concept j belongs to the determined mobile application category, R(concept i ,concept j ) is the relevance between the mobile application category concept i and the mobile application category concept j , w concept j app n is a weight value of the mobile application app n under the mobile application category concept j , comatt(app m ,app n ) is a number of identical attributes of the mobile application app n and the mobile application app m , and k is a preset impact factor.

19. The system of claim 12 , wherein the one or more programs includes instruction for respectively extracting mobile applications with top-ranked degrees of recommendation from the determined mobile application category, ranks the extracted mobile applications in an order from high to low of the degree of recommendation, and takes a predetermined number of top-ranked mobile applications as the recommendation result.

20. A non-volatile computer storage medium including at least one program for pushing a mobile application when implemented by a processor, comprising: instruction for calculating, according to information about mobile applications operated by each user in a mobile application store and a pre-generated relevance between the mobile applications, a relevance between mobile application categories in the mobile application store, the relevance between first and second mobile application categories of the mobile application categories being based on: multiplying a coefficient with the pre-generated relevance between each pair of the mobile applications in the first and second mobile application categories, the each pair including two mobile applications respectively from the first and second mobile application categories; and summing a result of the multiplying of each pair; instruction for obtaining a designated mobile application searched or downloaded from the mobile application store by a user; instruction for determining more than one mobile application category with top relevance to a mobile application category to which a designated mobile application belongs; instruction for calculating, according to pre-generated weight values of mobile applications, a degree of recommendation of each mobile application under the determined mobile application category; and instruction for selecting, according to a principle of high to low of the degree of recommendation of each mobile application under the determined mobile application category, a preset recommendation result number of mobile applications as a recommendation result and pushing the recommendation result.